Our goal is to ensure the semantic content well-preserved, whilst the target style is transferred in
the newly generated data.
For the applications of classification, these transferred data should be able to
cheat the state-of-the-art classifier \(\mathcal {F}_0\)  trained from the dataset without these instances.
With these transferred data included, we retrain the model for a new classifier \(\mathcal {F}_1\) .
It would be more robust and have better generalization.
Therefore, the value of the style transfer model
can be estimated using the prediction accuracy increase with the transferred data included.
Let \(P_0\)  and \(P_1\)  be the accuracy of \(\mathcal {F}_0\)  and \(\mathcal {F}_1\)  on the same testing set,
and \(P_1-P_0\)  is the value of the style transfer model.
Equivalently, we can evaluate the performance of the same classifier \(\mathcal {F}_0\) 
on the generated data from our transfer model, and the synthesized data from the simple sound mixing.
Given the same pairs of content and style audios,
the transfer model generates \(\mathcal {D}_t\)  and the simple mixing model produces \(\mathcal {D}_m\) ,
Suppose \(\mathcal {F}_0\)  gives the prediction accuracy of \(P_t\)  and \(P_m\) , respectively.
The lower of the prediction accuracy \(P_t\)  relative to \(P_m\) , the better of the style transfer model.
The idea forms a contrast to the proposed transfer strength in text style transfer[1]}.
The text style transfer requires the generated data recognizable,
and it is very sensitive to style noise.
But our purpose is different, we are concentrating on data augmentation.
If we can generate “high quality" data to beat \(\mathcal {F}_0\) ,
we succeed.
